Digital Disruptions: SMART Manufacturing Reimagined

The transition of Manufacturing from a Product centric process to Manufacturing as a Service (MaaS), offers a truly disruptive aspect of manufacturing. The development and adoption of the Internet of Things (IoT) is a critical element of smarter manufacturing. Though manufacturing companies have been implementing sensors and computerized automation for decades, the sensors, Programmable Logic Controllers (PLC) and PC-based controllers and management systems are largely disconnected from IT and operational systems. These systems are organized in hierarchical fashion within individual data silos and often lack connections to internal systems. There are several reasons for these legacy structures, including significant security issues. These legacy structures differ from the open, highly connected IP network structures that play such a large role in the value propositions of IoT. While the transition to more open network architectures and data sharing of IoT poses challenges in manufacturing and industrial markets, the combination of IoT, Big Data, and M2M optimization brings profound opportunities. IoT describes a system where items in the physical world, and sensors within or attached to these items, are connected to the Internet via wireless and wired network connections. The Internet of Things (IoT) connects and share data from inanimate objects.

SMART manufacturing is defined as ability to solve current or future manufacturing problems via an open and collaborative platform which allows fast paced creation and implementation of solutions

There is much talk about “Smart Manufacturing” being used as buzz word however let us take a deeper dive to understand what it is. SMART manufacturing is defined as “Ability to solve current or future manufacturing problems via an open and collaborative platform which allows fast paced creation and implementation of solutions”. The transformative changes using smart manufacturing are applicable for both types of manufacturing i.e. In-house manufacturing or contract manufacturers. As we review SMART Manufacturing, let us review “Digital Plant of the Future” and some of these concepts in detail and explore the possibilities of its applicability for In-house manufacturing as well as contract manufacturing.

I would like to share some of my experiences in the areas of “Knowledge Based Manufacturing” which is one of the most critical aspects of “Digital Plant of the Future”

• Capturing and leveraging structured & unstructured data in the form of quantitative (Metrics, KPIs, MES data), as well as qualitative (work plans, Process Instructions, Heuristics and more.)

• Collaboration as a tool to leverage information across global network of plants, warehouses, vendors, customers and more.

• Select vendors can track the health of replacement parts and initiate replacements proactively, which can be synchronized with planned downtimes to reduce downtime and breakdowns

• Manufacturing Shared Services

• Having a shared pool of talent can significantly drive down the number of resources and optimize the utilization of talented resources in the organization
• Maintenance and Quality Planning
• Shared services to extended ecosystem
• Shared pool of experts
• Shared pool of resources for remote monitoring and reporting of critical manufacturing processes
• Shared pool of analysts for Manufacturing Data Analytics and Predictions
• Sourcing for Capital Spend

• Financial impact and its considerations using real life models makes it a very powerful decision making tool. Accurate financial information becomes a critical tool for knowledge based manufacturing. Some of the key considerations include:

•Modeling the relationship between operational parameters (PR, inventory, material variance etc.) to the P&L impact is important
•A relationship tree can be constructed between operational parameters and respective line items in Plant P&L
•The model can be used to understand month-end P&L impact at the current level of performance.
•KPIs/Parameters can be adjusted to understand the impact on P&L in run-time
•Data mining, contextual text search and making proactive decisions based on available data mining tools help make manufacturing decisions smarter.
•Exceptions and alert handling on a proactive manner helps in informed decisions in manufacturing.

One of the possible frameworks of knowledge based manufacturing may be considered as follows for manufacturing plants and its subsequent integration with other business functions.

While applying the above-mentioned concepts of Smart Manufacturing, one should keep in mind about the subtle differences between In-house manufacturing and contract manufacturing.

SMART Manufacturing using IOT Where’s the Value in IoT for Manufacturing?

By connecting machines, a manufacturer can create intelligent networks along the entire value chain that communicate and control each other autonomously with significantly reduced intervention by operators. One can describe an IoT-enabled vision where machines predict failure and trigger maintenance processes autonomously rather than relying on unreliable monitoring by maintenance personnel. Another IoT example is self-organized logistics that react to unexpected changes in production, such as materials shortages and bottlenecks. Manufacturers will use technology to deliver dynamic, efficient, and automated manufacturing processes.

Summary

Compared to other business processes such as sales or procurement domains; manufacturing domain has been slow to embrace the world of digital disruptions or transformations; however recent innovations have evinced interest due to the opportunity to save cost as well as create an opportunity to generate additional revenue.

SMART Manufacturing will redefine the way “Blue-Collar” manufacturing is being seen across the world, as finally time has come to realize Knowledge Based Manufacturing combined with recent advancements of IoT. Several manufacturing organizations have started with baby steps in the direction, however much more is yet to be done in near future.